The present disclosure relates to household portfolio simulation and analysis to provide retirement income. In particular, it discloses advanced technologies for modeling, simulation and analysis of potential economic futures, as applied to household retirement prospects.
Many books are available to introduce risk budgeting and portfolio simulation to portfolio advisors, such as Pearson, N. 2002. Risk Budgeting: Portfolio Problem Solving with Value-at-Risk. New York: Wiley & Sons; Meucci, A. 2005. Risk and Asset Allocation. New York: Springer-Verlag; and Scherer, B. 2004. Portfolio Construction and Risk Budgeting, Second Ed. London: Risk Books. While these books are very technical and present mathematically sophisticated treatments of institutional portfolios, the problem of simulating and analyzing household portfolios is much more difficult than institutional portfolios. Factors that complicate individual portfolio include limited analytical resources, household objectives that change over a lifetime, prioritization of individual and household objectives, and complex income flows and spending goals. Tax minimization presents more issues and alternatives for a household than for an institutional portfolio. The simple objective of “maximize wealth” and the assumption of “non-satiation” (that dollars are of equal value, regardless of whether monthly income is $1,000 or $10,000 or $100,000) do not suit household portfolios.
Shortcomings of prior household portfolio simulation and modeling approaches reduce the resulting analysis to a general indication of the direction that a retirement plan will go, without enough accuracy to be used as a plan that can be implemented. In other systems, goals are typically mapped independently (1-to-1) with portfolios or sub-accounts, making it difficult for advisors and their clients to understand the interdependencies between goals and make important trade-off decisions. A limited view of the investor's resources is provided, which excludes certain asset classes, product types, assets held away, and major liabilities, frequently providing an overly optimistic or pessimistic view of the likelihood of meeting financial goals. Investment plans assume a static, fixed asset allocation over time, even though the client's investment strategy should be adjusted as their investment time horizon decreases and other changes impact the investment strategy. Investment planning applications are poorly integrated with the financial institution's investment product offerings and investment policies. Goal planning models are typically deterministic and formulaic (vs. stochastic and personalized). Output appears canned or “cookie cutter.” Together, these factors typically yield investment plans which are not properly aligned with an investor's true objectives and preferences, and are difficult to manage over time as the investor's resources, investment needs, goals, and priorities shift.
A preliminary discussion of household portfolio treatment by some of these inventors appeared as Torre & Rudd, “The Portfolio Management Problem of Individuals: A Quantitative Perspective,” Journal of Wealth Management (Summer 2004). It was discussed in a keynote speech at the IMN World Series of ETFs, by Andrew Rudd, “A New Approach to Financial Planning,” on Mar. 31, 2005.
An opportunity arises to improve on household portfolio management. In one aspect, concrete goals such as “pay for 100% of Susan's public college BA degree, which currently would cost $XX,000 a year” or “partially retire between age 55 and 65, with a partial retirement income of $XX,000 a year” can be elicited and automatically converted into future financial constraints. Priorities among concrete goals can be elicited, such as goals rated primary, secondary, and additional goals. Levels of satisfaction of concrete goals can be elicited, such as levels rated planned, minimum and better. In another aspect, the potential for unemployment can be evaluated by simulation, reflecting the chance and potential duration of a reversal of fortune. A further aspect is that results of simulations can be reported in terms of complete or partial accomplishment of prioritized goals. Better simulations of future economic impacts and more readily understood measures of effectiveness for alternative portfolio strategies result from various embodiments and combinations of aspects of this technology.